نشریه ماشین های کشاورزی
سال هشتم شماره 1 (پیاپی 15، نیمسال اول 1397)

IntroductionCurrent study tries to find a new simple and practical real-time technique to estimate forage crop nutritional quality indices at field conditions. Estimating these indices help producers to have field quality variation layer to reach the goals of Precision Agriculture. Previous studies have shown that standardized shear characteristics of crop stem would be a good indicator for some nutritional quality indices. In previous studies, laboratory tests were conducted at controlled conditions of crop moisture content, stem diameter and employing standard shear test procedure.
Materials and MethodsIn order to simulate field conditions, a two-stage study was conducted in Iran and United States. In the first stage fresh and naturally sun dried alfalfa stems were used in evaluating four levels of crop growth stage and eight loading conditions (four loading rates and two stem conditions). In order to evaluate the effectiveness of shear technique with respect to the conventional harvest method in Iran, shear tests were conducted using fixed and moving knives of a standard square hay baler (John Deere-348). Special fixtures were constructed to attach these knives to a universal testing machine (SANTAM, STM-20). Since evaluation of the suggested method with regard to other quality related factor indices such as different varieties and seeding rates, was not practically feasible in Iran in the second stage of this research, five different varieties and three seeding rates were tested in United States. In this part of the study, shear tests were conducted using modified Varner-Bratzler shear test with universal testing machine (TESTRESOURCES-311). Based on the results of loading rate and stem condition in the first stage, shear tests were carried out using loading rate of 500 mm/min and multiple stem condition. In both stages Specific Shear Energy (shear energy per stem diameter, J mm-1) were calculated using trapezoidal method. In order to compare the shear energy results with crude fiber nutritional quality indices such as Acid Detergent Fiber (ADF), Neutral Detergent Fiber (NDF) and Relative Feed Value (RFV), all alfalfa samples were analyzed using (Association of Official Agricultural Chemists) AOAC standard analytical laboratory methods. Statistical analyses were consisted of ANOVA mean comparison test at each level of factors and regression analysis to find the correlation between specific shear energy and nutritional quality indices.
Results and DiscussionsResults of ANOVA analysis and mean comparisons showed a significant difference in specific shear energy at different levels of loading rates. The higher loading rates showed lower energy which was related to lower ability of knives to cut alfalfa stem thoroughly and shredding the stems at lower speed levels. Significant differences were found in different levels of annual growing cycle, harvest time and seeding rates. Alfalfa stem in fifth harvest year showed the highest shear energy due to higher lignification in plant stems. In the first year, harvested alfalfa stem did not have the lowest shear energy which might be due to existence of weeds in first year field. Results showed higher values of shear energy in fifth harvest of the season in comparison with the third harvest which was acceptable because of differences in plant received Degree Day in these harvest times. The lowest seeding rate (5 kg h-1) showed the highest shear energy respect to the other seeding rates. The reason for this significant difference could be due to lower competition to receive water and nutritions, also lower plant density helps the canopy to receive more sun light which causes higher lignification. Comparing the shear energy means in different varieties didn’t show significant differences which can be explained because of varieties adoptability to the region specific weather condition. The regression analysis showed good correlations between specific shear energy and crude fiber nutritional indices (ADF, NDF and RFV). The negative trends which were found in regression analyses were also reported in similar studies.
ConclusionsTwo stage laboratory tests were conducted in order to evaluate the effect of alfalfa nutritional feed quality indices related factors on unitized shear energy. Results showed a significant difference of standardized shear energy mean at different levels of harvest time, annual growing cycle and seeding rates. The specific shear energy was not significantly different in different varieties because of varieties environmental adoptability. The unitized shear energy showed a good correlation with crude fiber related indices with similar trends in both stages of research and good agreements with previous studies.

IntroductionWith increase in world population, one of the approaches to provide food is using site-specific management system or so-called precision farming. In this management system, management of crop production inputs such as fertilizers, lime, herbicides, seed, etc. is done based on farm location features, with the aim of reducing waste, increasing revenues and maintaining environmental quality. Precision farming involves various aspects and is applicable on farm fields at all stages of tillage, planting, and harvesting. Today, in line with precision farming purposes, and to control weeds, pests, and diseases, all the efforts of specialists in precision farming is to reduce the amount of chemical substances in products. Although herbicides improve the quality and quantity of agricultural production, the possibility of applying inappropriately and unreasonably is very high. If the dose is too low, weed control is not performed correctly. Otherwise, If the dosage is too high, herbicides can be toxic for crops, can be transferred to soil and stay in it for a long time, and can penetrate to groundwater. By applying herbicides to variable rate, the potential for significant cost savings and reduced environmental damage to the products and environment will be possible. It is evident that in large-scale modern agriculture, individual management of each plant without using some advanced technologies is not possible. using machine vision systems is one of precision farming techniques to identify weeds. This study aimed to detect three plant such as Centaurea depressa M.B, Malvaneglecta and Potato plant using machine vision system.
Materials and MethodsIn order to train algorithm of designed machine vision system, a platform that moved with the speed of 10.34 was used for shooting of Marfona potato fields. This platform was consisted of a chassis, camera (DFK23GM021,CMOS, 120 f/s, Made in Germany), and a processor system equipped with Matlab 2015 version. The video camera was installed in 60-centimeter height above the ground level. Therefore, all plants in the camera field of view (whether on the crops row or between the rows) were analyzed. This study conducted on 4 hectares of potato fields in Kermanshah–Iran (longitude: 7.03 E; latitude: 4.22 N). The most suitable color space for segmentation plants was HSV color space and most suitable channel of applying threshold was the H channel. In this study, features in two areas of color features, texture features based on gray co-occurrence matrix were extracted. Ultimately, 126 color features and 80 texture features were extracted from each object. In final six features among 206 features were selected.
Results and DiscussionAmong 206 extracted features, six effective features including the additional second component of the YCbCr color space, green index minus blue in RGB color space, sum entropy in the neighborhood of 45 degree, diagonal moment in the neighborhood of 0 degree, entropy in the neighborhood of 45 degree, additional third component index in CMY color space were selected using hybrid ANN-PSO. This means that, two set features have the same effect over plants. The result shows that hybrid ANN-SAGA classified Centaurea depressa M.B, Malvaneglecta and Potato plant with 99.61% accuracy. This accuracy is high and this meant that 1. These plants have different 6 selected features, 2. The classifier is very powerful to classify.
Conclusions1. Plants with similar features make the classification process complicated and less accurate.
2. The presence of shadow on the plants’ leaves reduces the accuracy of the classification.

Introduction Today, hybrid seeds are expensive because the company that produces them spends a lot of money on research and development that often takes years to accomplish. So precise planting of seeds in order to create the best growing condition for all seeds is important. Modified size and shape of seeds for precision planting, providing macro and micro nutrients since the start of seed germination and control pests and diseases are goals that are possible by coating seeds. The overall process of seed coating or seed pelleting comprises a number of important stages: 1- Droplet formation 2- Droplet travel 3- Impingement 4- Wetting 5- Spreading 6- Coalescence. Seed coating was largely borrowed from the confectionery industry which had developed this technique over the ages and is still widely used today. The seed industry concentrated on using the rotary drum or pan. This type of pan or drum was used for batches of up to 150–200 kg. Some rotary drum coater were developed subsequently which improved handling, particularly in the way the drying air was introduced and extracted. The pan of drum rotary coater is placed at the end of a tilted rotating shaft that is turned at a constant speed about 15- 20 rpm. Then a nozzle is spraying proper amount of coating solution on the seeds. The aim of this study was to evaluate technology and determine the factors affecting its quality coverage.
Materials and Methods This experiment lay out in factorial experiment based on random complete block design with three replications. The first factor was vertically distance nozzle from seed bed in two levels 150 and 300 mm, second factor was the nozzle installed location in two levels installed in 1/4 diameter upper center and in center of cylinder, and third factor was concentration of binder polyvinylpyrrolidone (PVP) in three levels 50, 75 and 100 g kg-1 kaolin. In order to measure the pellet error percent, first 100 pellets were selected and broken. No seed or multi-seed pellets were counted. For measuring physical strength of pellets, instron machine were implemented in physical properties laboratory in Aborihan department of Tehran University. Its load cell capacity was 490 N. Forward speed of the instron was 5 mm per minute. Germination test were performed in the laboratory in dryland agricultural substitute Sararood, Kermanshah.
Results and Discussion The results showed that the nozzle distance from the seed bed had a significant effect on all measured traits (1% level). With increasing distance from the seed bed, the physical strength of pellet and the percentage of pellet error decreased but germination increased. In fact, with increasing nozzle distance from 150 mm to 300 mm, the physical strength of pellet decreased from 22.8 N to 21.4 N, the pellet error decreased from 4.1% to 2.1% but germination increased from 71.3 to 73.4 percent. The used binder quantity had a significant effect on all measured traits (1% level). By increasing of using binder, the physical strength of pellet and the percentage of pellet error increased but germination strongly decreased. In the other word, with increasing used binder from 50g to 100g per one kilogram kaolin, the physical strength of pellet increased from 13.9N to 29.1N, the pellet error increased from.2.01 to 4.18 percent but germination decreased from 90.42 to 53.17 percent. The nozzle installed location had a significant effect only on the pellet error (1% level). In the other word, the nozzle installed on the cylindrical center is better than nozzle installed in 1/4 diameter upper center. There was negative significant correlation (r=-0.96) between physical strength shell characteristics and germination. So increasing the physical strength of the shell is reduced germination. There was a significant correlation (r= 0.621) between physical strength and pellet error percentage. So with increasing physical shell strength, pellet error was increased.

IntroductionOne of the problems which considered in recent years for grain harvesting is loss of wheat during production until consumption and tenders the offers for prevention of its especially in harvesting times by combine harvesting machine. Grain harvesting combines are good examples of an operation where a compromise must be made. One would expect increased costs because of natural loss before harvesting, because of cutter bar loss, because of threshing loss, because of greater losses over the sieve and because of the reduced forward speed necessary to permit the through put material to feed passed the cylinder. The ability to recognize and evaluate compromise solutions and be able to predict the loosed grain is a valuable trait of the harvesting machine manager. By understanding the detailed operation of machines, be able to check their performance, and then arrive at adjustments or operating producers which produce the greatest economic return.
Voicu et al. (2007) predicted the grain loss in cleaning part of the combine harvester by using the laboratory simulator based on dimensional analysis method. The obtained model was capable to predict the grain loss perfectly.
Soleimani and Kasraei (2012) designed and developed a header simulator to optimize the combine header in rapeseed harvesting. Parameters of interest were: forward speed, cutter bar speed and reel index. The results showed that all the factors were significant in 5% probability. Also in the case of forward speed was 2 km h-1, cutter bar speed was 1400 rpm and reel index was 1.5, the grain loss had minimum quantity.
The main purpose of this research was to develop an equation for predicting grain loss in combine header simulator. Modeling of the header grain loss was conducted using dimensional analysis approach.
Effective factors on grain loss in combine header unit were: forward speed, reel speed and cutter bar height.
Materials and MethodsFor studying the effective parameters on head loss in grain combine harvester, a header simulator with the following components was built in Biosystems Engineering Department of Shiraz University.
Reel unitThe reel size was 120 cm length and 100 cm diameter. This reel was removed from an old combine header and installed on a fixed bed. For changing the rotational speed of the reel, an electrical inverter (N50-007SF, Korea) was used.
Cutter bar unitThe cutter bar length was 120 cm. Knifes were installed on this section. Reciprocating motion was transmitted to the cutter bar through a slider crank attached to a variable speed electric motor (1.5kw, 1400 rpm, Poland). The motor was fixed on the bed.
Feeder unitThis section was consisted of a rail and a virtual ground. This ground was a tray that the wheat stems were staying on it manually. The rail was the path of virtual ground.
Treatments consisted of three levels of rotational speed of reel (21, 25 and 30 rpm), three levels of forward speed of virtual ground (2, 3 and 4 km h-1), three levels of cutter bar height (15, 25 and 35 cm) and three replications. In other words, 81 tests were done. The basis of choosing levels of treatments was combine harvester manuals and driver’s experiences.
The dependent variable (H.L) was calculated as below:(1)
Where L.G is the mass of loss grains and H.G is the mass of harvested grains.
Results and DiscussionGenerally results of ANOVA test showed that the cutter bar height, rotational speed of reel and forward speed had significant effect on head loss. Also interaction of rotational speed and forward speed, cutter bar height and forward speed had significant effect on head loss. These findings were based on Soleimani and Kasraei (2012) research. Therefore, the cutter bar height, rotational speed of reel and forward speed were three independent parameters on head loss as a dependent parameter.
By results of laboratory data, the equation for predicting grain loss by header simulator was obtained.
ConclusionsThe statistical results of F- test in 5% probability showed that there were no significant difference between measured and predicted amounts for laboratory data.

IntroductionThe compaction of soil by agricultural equipment has become a matter of increasing concern because compaction of arable lands may reduce crop growth and yield, and it also has environmental impacts. In nature, soils could be compacted due to its own weights, external loads and internal forces as a result of wetting and drying processes. Soil compaction in sugarcane fields usually occurs due to mechanized harvesting operations by using heavy machinery in wet soils. Adding plant residues to the soil can improve soil structure. To improve soil physical quality of sugarcane fields, it might be suggested to add the bagasse and filter cake, which are the by-products of the sugar industry, to the soils.
When a soil has been compacted by field traffic or has settled owing to natural forces, a threshold stress is believed to exist such that loadings inducing lower than the threshold cause little additional compaction, whilst loadings inducing greater stresses than the threshold cause much additional compaction. This threshold is called pre-compaction stress (σpc). The σpc is considered as an index of soil compactibility, the maximum pressure a soil has experienced in the past (i.e. soil management history), and the maximum major principal stress a soil can resist without major plastic deformation and compaction. Therefore, the main objective of this study was to investigate the effects of wetting and drying cycles, soil water content, residues type and percent on stress at compaction threshold (σpc).
Materials and MethodsIn this research, the effect of adding sugarcane residues (i.e., bagasse and filter cake) with two different rates (1 and 2%) on pre-compaction stress (σpc) in a silty clay loam soil which was prepared at two relative water contents of 0.9PL (PL= plastic limit, moist) and 1.1PL (wet) with or without wetting and drying cycles. This study was conducted using a factorial experiment in a completely randomized design with three replications. A composite disturbed sample of topsoil (0–200 mm deep) of a silty clay loam soil was collected from Isfahan province (32 31.530 N; 51 49.40E) in center of Iran. The mean annual precipitation and temperature of the region are about 160 mm and 16 C, respectively. Sugarcane residues (bagasse and filter cake) were obtained from the sugarcane fields in Ahvaz, Khuzestan province (Iran). The samples were air-dried and passed through a 2-mm sieve. Soil treated by bagasse and filter cake in different rates was poured and knocked lightly into cylinders with diameter and height of 25 and 8 cm, respectively. Large air-dry disturbed soil samples were prepared and some of them were exposed to five wetting and drying cycles. Finally, the soil surface was covered by a plastic sheet and was left overnight in the laboratory (for 24 hours) to enable the moisture to equilibrate. The loading tests were performed the next day. The pre-compaction stress was determined by plate sinkage test (PST). The loading test for PST was performed using CBR apparatus. The compression for PST was continuous at the same constant displacement rate of the CBR (i.e. 1 mm min-1). Determination of the σpc was done using Casagrande’s graphical estimation procedure (Casagrande, 1936) in a program written in MatLab software.
Results and DiscussionThe results showed that σpc was significantly decreased by adding residues to the soil at both water contents, and with/without wetting and drying process. For untreated treatments (control), the σpc decreased with increasing water content. Although σpc decreased with adding the residues to the soil, however, the effect of residue types and percentages and soil water content on σpc was not significant for the soil samples treated with residues.
ConclusionsIn order to prevent re-compaction of the soil and improve its structure, it is suggested that traffic control system with permanent routes for the movement of machinery to be used in sugar cane plantations and the residues (after desalination) to be added into strips that are placed under cultivation.

IntroductionNo use of advanced mechanization and weakness in post harvesting technology are the main reasons of agricultural losses. Some of these wastes (agricultural losses) are related to crop growing conditions in field and the remaining to processing of sugar in mill. The most useful priority setting methods for agricultural projects are the Analytic Hierarchy Process (AHP). So, this study presents an introduction of application manner of the AHP as a mostly common method of setting agricultural projects priorities. The purpose of this work is studying the sugarcane loss during production process using AHP in Khuzestan province.
Materials and MethodsThe resources of sugarcane waste have been defined based on expert’s opinions. A questionnaire and personal interviews have formed the basis of this research. The study was applied to a panel of qualified informants made up of thirty-two experts. Those interviewed were distributed in Sugarcane Development and By-products Company in 2015-2016. Then, with using the analytical hierarchy process, a questionnaire was designed for defining the weight and importance of parameters effecting on sugarcane waste. For this method of evaluation, three main criteria considered, were yield criteria, cost criteria and income criteria. Criteria and prioritizing of them was done by questionnaire and interview with sophisticated experts. This technique determined and ranked the importance of sugarcane waste resources based on attributing relative weights to factors with respect to comments provided in the questionnaires. Analytical Hierarchy Process was done by using of software (Expert choice) and the inconsistency rate on expert judgments was investigated.
Results and DiscussionHow to use agricultural implements and machinery during planting and harvesting of sugarcane, can increase or decrease the volume of waste. In planting period, the losses mainly consists of loss of setts during cutting them by machine, injury the setts by biological and physical agents, loss of growth in sett field, unsuitable sett covering and replanting the gaps. During cultivation period the losses include late in field harvesting and so late in regrows the cane, unsuitable ratooning and use of cultivator, varying the size of the furrows and ricks in around the field and destroyed the stubbles during rationing. In harvesting the losses easily seen and mainly associated by efficiency of harvester machines. Billets loss of the fleet in the transmission roads toward mill and late in harvest the burnet cane and then transport to mill are main sources of quantities and qualities of losses. The Expert Choice software performed well in conjunction with the panel of experts for choosing the criteria and assigning weights under the AHP methodology. According to the results, effective parameters on sugarcane waste consist of caused by harvesting, transportation, industry, planting, preserve operations, ratooning and land preparation. Weight of effective criteria (yield, cost and income) on losses of sugarcane obtained from paired comparison in the experts’ view which has been calculated with Expert choice software. The result of this survey by AHP techniques showed that yield criteria had the most and income criteria had the least importance for expert in sugarcane production. In this stage of research, alternatives of paired comparison relative to criteria was separately formed and information of questionnaire which relates to paired comparison of criteria was obtained. Between effective parameters on losses of sugarcane, harvesting with 0.243 weighted average was the most effective factor and transportation with 0.187 weighted average, industry with 0.179 weighted average, planting with 0.156 weighted average, preserve operations with 0.109 weighted average, ratooning with 0.071 weighted average, and land preparation with 0.055 weighted average was later, respectively (Inconsistence Rate =0.04). The results are examined by monitoring sensitivity analysis while changing the criteria priorities. Since different judgments are made on comparison of criteria, we use sensitivity analysis in order to provide stability and consistence of analysis. With increasing or decreasing of the criteria, we will conclude that ratio of other indices will not change.
ConclusionsThis paper looks at AHP as a tool used in Sugarcane Agro-Industries to help in decision making. Results show that criteria studied in this research can help prioritizing of loss resources during sugarcane production process. According to the results, effective parameters on sugarcane waste consist of caused by harvesting, transportation, industry, planting, preserve operations, ratooning and land preparation.

IntroductionPomegranate (Punica grantum L.) is classified into the family of Punicaceae. One of the most influential factors in postharvest life and quality of horticultural products is temperature. In precooling, heat is reduced in fruit and vegetable after harvesting to prepare it quickly for transport and storage. Fikiin (1983), Dennis (1984) and Hass (1976) reported that cold air velocity is one of the effective factors in cooling vegetables and fruits. Determining the time-temperature profiles is an important step in cooling process of agricultural products. The objective of this study was the analysis of cooling rate in the center (arils) and outer layer (peel) of pomegranate and comparison of the two sections at different cold air velocities. These results are useful for designing and optimizing the precooling systems.
Materials and MethodsThe pomegranate variety was Rabab (thick peel) and the experiments were performed on arils (center) and peel (outer layer) of a pomegranate. The velocities of 0.5, 1 and 1.3 m s-1 were selected for testing. To perform the research, the cooling instrument was designed and built at Department of Biosystems Engineering of Tabriz University, Tabriz, Iran. In each experiment six pt100 temperature sensors was used in a single pomegranate. The cooling of pomegranate was continued until the central temperature reached to 10°C and then the instrument turned off. The average of air and product temperatures was 7.2 and 22.2°C, respectively. The following parameters were measured to analyze the process of precooling: a) Dimensionless temperature (θ), b) Cooling coefficient (C), c) Lag factor (J), d) Half-cooling time (H), e) Seven-eighths cooling time (S), f) Cooling heterogeneity, g) Fruit mass loss, h) Instantaneous cooling rate, and i) convective heat transfer coefficient.
Results and DiscussionAt any air velocity, with increasing the radius from center to outer layer, the lag factor decreased and cooling coefficient increased. Also, half-cooling time and seven-eighths cooling time reduced and so cooling rate enhanced. Thus, despite a reduction lag factor, due to a significant increase in cooling coefficient, half and seven-eighths cooling declined. Dimensionless temperature, θ, less than 0.2 and 0.1 in the center and peel and at different velocities had little impact on the rate of cooling in pomegranate. The difference in primary cooling time (0-500 sec) and in high lag factor (greater than 1) occurred, which represents an internal resistance of heat transfer in fruit against the airflow. Cooling the center of pomegranate starts with time delay which causes the beginning of the cooling curve becomes flat. Seven-eighths cooling time is the part of half-cooling time. The range of S was 2.5-3.5H in the present study. At first, cooling heterogeneity at 0.5 m s-1 was low in the center and peel of pomegranate and then with increasing the velocity up to 1 m s-1, it enhanced and again decreased at 1.3 m s-1. After a period of cooling (5000 sec), almost layers of pomegranate reached the same temperature and so heterogeneity reduced. The maximum instantaneous cooling rate was 8.09 × 10-4 ºC s-1 at 1.3 m s-1 in the center of pomegranate. By increasing the airflow velocity from 0.5 to 1.3 m s-1, the convective heat transfer coefficient increased from 11.05 to 17.51 W m-2 K-1. Therefore, the velocity of cold air is an important factor in variation of convective heat transfer coefficient.
ConclusionsCooling efficiency is evaluated based on rapid and uniformity of cooling. Cooling curves against time reduced exponentially at the different airflow velocities in the center (aril) and outer layer (peel) of pomegranate. By increasing the air flow velocity, half and seven-eighths cooling time reduced and cooling rate increased that showed direct impact of this variable. The main reason was the variation of convective heat transfer coefficient. The lowest level of uniformity obtained at the highest velocity (1.3 m s-1), which made more uniform temperature distribution in the fruit. The results showed that applied method in this experiment could be used for the fruits which are similar to sphere and could explain the unsteady heat transfer without complex calculations in the cooling process. Based on the results of this research, the airflow velocity of 1.3 m s-1 is recommended for forced air precooling operations of pomegranate.

IntroductionToday, most of the agricultural machines for doing agricultural operations and covering the entire farm, must move in the farm, and travel a certain distance without doing anything useful. Common agricultural machines are controlled by human beings using habits, machinery models, and personal experiences without using computer-based tools. This trend leads to repetitive patterns and affect farm efficincy. Therefore, applying optimization techniques in determining the optimum pattern and track for on-farm machinery would be very effective.
One of the main problems of conventional movement patterns on farms is the time wasted on moving towards the end of the field, which will have a big impact on field efficiency. The purpose of this study is to reduce the useless distance traveled by agricultural machines using genetic algorithm while moving on the farm and going from one track to the next, and, consequently, increase farm efficiency.
Materials and MethodsIn this study, the rectangle farm that was 80 meters wide and had an arbitrary length was selected for simulation, and different types of turning methods were tested. The calculations and simulation were based on genetic algorithm using the MATLAB 2013 software. In this case, the minimum traveled distance was set as solution evaluation criterion. The solutions were applied and simulated according to these assumptions: Each gene was considered a track number, and the algorithm’s chromosomes were produced by connecting all the tracks to each other,. The width of each track was considered equal to the width of the machine, and based on reproduction parameters such as population size and the number of repetitions, a percentage of the children were produced through point intersection and another percentage were produced through mutation. In determining the distance between the tracks, Ω or T or U were used for two adjacent tracks, U was used for two tracks that had a track between them, and a longer U was used for tracks that had more than one track between them.
Based on the number of the initial population, the chromosomes that were supposed to be parents at the beginning were selected. The children produced new population was created and the above steps were repeated. During the last repetition, the best child chromosome was introduced as the answer.
In order to calculate the effects of different methods of turning on the non-working distance covered during agricultural operations, the non-working distance traveled during 5 orders of movement, including 4 traditional orders (continuous, spiral, all-around, and blocked) and 1 smart order were compared to each other.
In the continuous pattern, because movement continues in the next track at the end of each track, all the turnings are inevitably done in the Ω way, and thus a greater distance is travelled compared to the U way. In the spiral pattern, the distance travelled in turnings between different tracks on the farm is equal. In the all-around pattern, movements are done from the sides and the operation is concluded at the center of the farm; therefore, the long U method of movement is used at the end of all the tracks, and Ω turning is used for the last track at the center of the farm. In the blocked pattern, the farm is devided into two or more blocks, and the all-around movement pattern is used in each block as an independent farm. In the smart movement pattern, the beginning and ending of the agricultural operations are considered in the vicinity of the hypothetical road which, in addition to facilitating access to the road, had a significant impact on reducing the useless distance traveled on the farm.
Results and DiscussionThe final optimum pattern was compared to traditional patterns in the form of charts. The optimum pattern of movement which uses smart genetic algorithm and avoids long turning methods (such as, Ω and T) leads to reduced wasted time and distance traveled by agricultural machines and increased field efficiency and also, decreasing the non-working traveled distance and waste time approximately, 45 % and 47 % respectively. This is due to avoiding turning methods of Ω and T (compared to the U method). Also, the fatigue resulting from these approaches and their wasted time is greater than U method used in the genetic algorithm pattern.
ConclusionsThe optimum pattern of movement which uses smart genetic algorithm was compared to conventional patterns that showed significant saving in non- working distance and waste time in farm. This optimum pattern can be implemented in automatic navigation but there is the possibility of its implementation by operators in common navigation.

IntroductionSoil compaction is one of the most destructive effects of machine traffic. Compaction increases soil mechanical strength and reduces its porosity, plant rooting and ultimately the yield. Nowadays, agricultural machinery has the maximum share on soil compaction in modern agriculture. The soil destruction may be as surface deformation or as subsurface compaction. Any way machine traffic destructs soil structure and as result has unfavorable effect on the yield. Hence, soil compaction recognition and its management are very important. In general, soil compaction is the most destructive effect of machine traffic. Modeling of ecological systems by conventional modeling methods due to the multitude effective parameters has always been challenging. Artificial intelligence and soft computing methods due to their simplicity, high precision in simulation of complex and nonlinear processes are highly regarded. The purpose of this research was the modeling of soil compaction system affected by soil moisture content, the tractor forward velocity and soil depth by multilayer perceptron neural network.
Materials and MethodsIn order to carry out the field experiments, a tractor MF285 which was equipped with a three-tilt moldboard plough was used. Experiments were conducted at the Agricultural research field of University of Mohaghegh Ardabili in five levels of moisture content of 11, 14, 16, 19 and 22%, forward velocity of 1, 2, 3, 4 and 5 km/h, and soil depths of 20, 25, 30, 35 and 40 cm as a randomized complete block design with three replications. In this study, perceptron neural network with five neurons in the hidden layer with sigmoid transfer function and linear transfer function for the output neuron was designed and trained.
Results and DiscussionField experiments showed three main factors were significant on the bulk density (P

IntroductionAgricultural productions has been identified as a major contributor to atmospheric greenhouse gases (GHG) on a global scale with about 14% of global net CO2 emissions coming from agriculture. Identification and assessment of environmental impact in the production system will be leading to achieve the goals of sustainable development, which would be achieved by life cycle assessment. To find the relationship between inputs and outputs of a production process, artificial intelligence (AI) has drawn more attention rather than mathematical models to find the relationships between input and output variables by training, and produce results without any prior assumptions. The aims of this study were to life cycle assessment (LCA) of Alfalfa production flow and prediction of GWP (global warming potential) per ha produced alfalfa (kg CO2 eq.(ha alfalfa)-1) with respect to inputs using ANFIS.
Materials and MethodsThe sample size was calculated by using the Cochran method, to be equals 75, then the data were collected from 75 alfalfa farms in Bukan Township in Western Azerbaijan province using face to face questionnaire method. Functional unit and system boundary were determined one hectare of alfalfa and the farm gate, respectively. Inventory data in this study was three parts, included: consumed inputs in the alfalfa production, farm direct emissions from crop production and indirect emissions related to inputs processing stage. Direct Emissions from alfalfa cultivation include emissions to air, water and soil from the field. Data for the production of used inputs and calculation of direct emission were taken from the EcoInvent®3.0 database available in simapro8.2.3.0 software and World Food LCA Database (WFLD). Primary data along with calculated direct emissions were imported into and analyzed with the SimaPro8.2.3.0 software. The impact-evaluation method used was the CML-IA baseline V3.02 / World 2000. Damage assessment is a relatively new step in impact assessment. The purpose of damage assessment is to combine a number of impact category indicators into a damage category (also called area of protection). To assess the damage in this study, IMPACT 2002 V2.12 / IMPACT 2002 method was used. ANFIS is a multilayer feed-forward network which is applying to map an input space to an output space using a combination of neural network learning algorithms and fuzzy reasoning. In order to enable a system to deal with cognitive uncertainties in a manner more like humans, neural networks have been engaged with fuzzy logic, creating a new terminology called ‘‘neuro-fuzzy method. An ANFIS is used to map input characteristics to input membership functions (MFs), input MF to a set of if-then rules, rules to a set of output characteristics, output characteristics to output MFs, and the output MFs to a single valued output or a decision associated with the output. The main restriction of the ANFIS model is related to the number of input variables. If ANFIS inputs exceed five, the computational time and rule numbers will increase, so ANFIS will not be able to model output with respect to inputs. In this study, the number of inputs were ten, including machinery, diesel fuel, nitrogen, phosphate, electricity, water for irrigation, labor, pesticides, Manure and seed and GWP was as the model output signal. To solve this problem and employ all input variables, we proposed clustering input parameters to four groups. Correspondingly, the proposed model was developed using seven ANFIS sub-networks. To obtain the best results several modifications were made in the structure of ANFIS networks, and some parameters were calculated to compare the results of different models. Making a comparison between different topologies the employment of some indicators was a pivotal to get a good vision of various the structures, such as the correlation coefficient (R), Mean Square Error (MSE) and Root Mean Square Error (RMSE). In addition, for checking comparison between experimental and modeled data, the t-test was performed. The null hypothesis was equality of data average. To develop ANFIS models, MATLAB software (R2015a) was used.
Results and DiscussionImpact categories including Global warming potential (GWP), eutrophication potential (EP), human toxicity potential (HTP), terrestrial ecotoxicity potential (TEP), oxidant formation potential (OFP), acidification potential (AP), Abiotic depletion (AD) and Abiotic depletion (fossil fuels) were calculated as 13373 kg CO2 eq, 19.78 kg PO4-2 eq, 2054 kg 1,4-DCB eq, 38.7 kg 1,4-DCB eq, 3.84 kg Ethylene eq, 90.64 kg SO2 eq, 0.015 kg Sb eq and 205169 MJ, respectively. The results of damage assessment of alfalfa production revealed that electricity in three categories, human health damage, climate change and ecosystem quality had maximum role, but in the resources damage category was the largest share of damage related direct emissions. The value of the climate change was calculated as 13373 kg CO2 eq. The best structure was including five ANFIS network in the first layer, two network in the second layer and a network in output layer. Values of R, MSE and RMSE for the final ANFIS in k-fold model were 0.983, 0.107 and 0.327 and in C-means model were 0.999, 0.007 and 0.082, respectively. The p-value in t-test was 0.9987 that indicates non-significant difference between the mean of modeling and experimental data. Coefficient of determination (R2) between actual and predicted GWP based on the best k-fold and C-means models were 0.994 and 0.99, respectively. The coefficient of determination for these index demonstrated the suitability of the developed network for prediction of GWP of alfalfa production in the studied area.
ConclusionsBased on the results of this study, to reduce the emissions, electricity consumption should be reduced. Adapting of electro pumps power with the well depth and the amount of required water taken for field will be a possible solution to reduce the use of electricity in order to trigger of electro pumps and thus reducing of emissions related to it. In some situations, the type of mineral fertilizer is the main determinant of emissions at the whole farm level and changing the type of fertilizer could significantly reduce the environmental impact. Comparison of GWP modeling results using two methods of k-fold and C-means revealed that C-means method has higher accuracy in prediction of GWP. Also the high quantities for the determination coefficient related to both modeling methods demonstrates high correlation between actual and predicted data.

IntroductionThe color of soil depends on its composition and this feature is easily available and rather stable. Fast and accurate determination of soil organic matter distribution in the agricultural fields is required, especially in precision farming. General laboratory methods for determining the soil organic carbon are expensive, time-consuming with many repetitions, and high consumption of chemicals. Soil scientists use the Munsell soil color diagrams to define the soil color. Due to the nature of Munsell color diagrams; this system is less suitable for recognizing exact color of the soil because of weak relationship and limited number of chips. Fast methods like image processing, colorimetric and spectroscopy provide a description of most physical characteristics of the soil color. Some of the advantages of using digital cameras was used in this study, are simple sampling of screened soil, being free from chemicals and toxic materials and being fast, inexpensive and precise.
Materials and MethodsIn this research, 80 A-horizon (0-10 cm) soil samples were collected from various agricultural soils in west Azerbaijan, in the North West of Iran. Soil texture of these fields was loam clay and clay. The amount of organic carbon in samples was determined. The camera was installed at the distance of 0.5 m from the Petri dish on the lighting compartment. Captured images with the digital camera were saved in RGB color space. Processing operations were done by MATLAB 2012 software. The features extracted from the color images are used to model the soil organic carbon including the color features in different spaces. Four-color spaces including RGB, HSI, LAB and LUV were studied to find the relation between the color and the soil organic carbon.
Results and DiscussionThe correlation of R component in the RGB model shows a strong single-parameter relation with the organic carbon as R2=0.83. This good relationship can be due to the compound information of the red color component on both brightness and chromaticity dimension. The results also show that the organic carbon has a relatively strong correlation with the light parameters, intensity and lightness in the HSI, Lab and LUV color spaces respectively. It also has a weak correlation with other parameters, since they cannot have a proper linear correlation with organic carbon due to their structural nature. Results show that the highest correlation is obtained when the R and G components participate in modeling and the component B is omitted. One explanation of this high correlation could be the high sensitivity of component B to the intensity and the angle of light. Even a small change in light changes this component. Thus, in order to reduce the effect of this component, it is better to omit it from the models and make models independent of it. In next section, 51 data were used to train neural network, 14 data were used to test the network and 12 data for network validating. The amount of soil organic carbon was output of the neural networks that was estimated after training using the color component values of each segment. To assess the accuracy of the network, estimated values and actual values of each sample were plotted in a graph. The minimum MSE values were 7.28e-6 with 16 neurons, 3.77e-6 with 14 neurons, 4.8e-3 with 10 neurons and 3.77e-6 with 12 neurons for RGB, HSI, Lab and LUV color spaces respectively.
ConclusionsThe availability of digital cameras, possibility to use it in different situations, being inexpensive and providing many samples are the advantages of this method to estimate the soil organic carbon amount. Therefore, digital photography can be used as an analytical method to evaluate the soil fertility. It also requires a low cost of sample testing, and can provide a good possibility of time and place classification for studying a vast area. However to develop more reliable models, more effort is needed, such as collecting more soil samples of different areas that include a wide range of soil features.

IntroductionThe extensive use of diesel engines in agricultural activities and transportation, led to the emergence of serious challenges in providing and evaluating alternative fuels from different sources in addition to the chemical properties close to diesel fuel, including properties such as renewable, inexpensive and have fewer emissions.
Biodiesel is one of the alternative fuels. Many studies have been carried out on the use of biodiesel in pure form or blended with diesel fuel about combustion, performance and emission parameters of engines. One of the parameters that have been less discussed is energy balance.
In providing alternative fuels, biodiesel from waste cooking oil due to its low cost compared with biodiesel from plant oils, is the promising option. The properties of biodiesel and diesel fuels, in general, show many similarities, and therefore, biodiesel is rated as a realistic fuel as an alternative to diesel. The conversion of waste cooking oil into methyl esters through the transesterification process approximately reduces the molecular weight to one-third, reduces the viscosity by about one-seventh, reduces the flash point slightly and increases the volatility marginally, and reduces pour point considerably (Demirbas, 2009). In this study, effect of different percentages of biodiesel from waste cooking oil were investigated. Energy distribution study identify the energy losses ways in order to find the reduction solutions of them.
Materials and MethodsRenewable fuel used in this study consists of biodiesel produced from waste cooking oil by transesterification process (Table 1). Five diesel-biodiesel fuel blends with values of 0, 12, 22, 32 and 42 percent of biodiesel that are signs for B0, B12, B22, B32 and B42, respectively.
The test engine was a diesel engine, single-cylinder, four-stroke, compression ignition and air¬cooled, series 3LD510 in the laboratory of renewable energies of agricultural faculty, Tarbiat Modarres University. The engine is connected to a dynamometer and after reaching steady state conditions data were obtained (Fig. 1).
In thermal balance study, combustion process merely as a process intended to free up energy fuel and the first law of thermodynamics is used (Koochak et al., 2000). The energy contained in fuel converted to useful and losses energies by combustion. Useful energy measured by dynamometer as brake power and losses energy including exhaust emission, cooling system losses and uncontrollable energy losses.
Variance analysis of all engine energy balance done by split plot design based on a completely randomized design and the means were compared with each other using Duncan test at 5% probability.
Results and DiscussionResults showed that, in general, biodiesel use has a significant impact on all components of energy balance. Of total energy from fuel combustion, the share of energy losses to form of exhaust emissions the maximum value in all percentages allocated to biodiesel (Average 51.715 percent) with the maximum and minimum amount of B42 (55.982 percent) and B0 (46.481 percent), respectively (Fig. 2). Also, fuel blend with 12% biodiesel was diagnosed the best blend because of having the most useful power, having the lowest energy losses through the exhaust and cooling system.
ConclusionsUsing biodiesel produced from waste cooking oil by transesterification process, lead to increase the useful power. The addition of biodiesel to pure diesel cause to significant reduction in the waste energy due to friction. In higher amounts of biodiesel increase energy losses especially through the exhaust and cooling system due to higher viscosity.

IntroductionAnaerobic digestion (AD) is a process of breaking down organic matter, such as manure, in the absence of oxygen by concerted action of various groups of anaerobic bacteria. The AD process generates biogas, an important renewable energy source that is composed mostly of methane (CH4), and carbon dioxide (CO2) which can be used as an energy source. Biogas originates from biogenic material and is therefore a type of biofuel. Enhancement of biogas production from cattle dung or animal wastes by co-digesting with crop residues like sugarcane stalk, maize stalks, rice straw, cotton stalks, wheat straw, water hyacinth, onion waste and oil palm fronds as well as with liquid waste effluent such as palm oil mill effluent. Nevertheless, the search for cost effective and environmentally friendly methods of enhancing biogas generation (i.e. biogas yield) still needs to be further investigated. Many workers have studied the reaction kinetics of biogas production and developed kinetic models for the anaerobic digestion process. Objective of this study is to investigate the effect of biological additive using of organic loading rate (OLR) in biogas production from cow dung. In addition, cumulative biogas production was simulated using logistic growth model, and modified Gompertz models, respectively.
Materials and MethodsThe study was performed in 2015-2016 at the agricultural research center of Ardabil Province, Moghan (39.39 °N, 48.88° E). Fresh cow manure used for this research was collected from the research farm of the Institute for Animal Breeding and Animal Husbandry, Moghan. It was kept in 30 l containers at ambient temperature until fed to the reactors. In this study, experiments were conducted to investigate the biogas production from anaerobic digestion of cow manure (CM) with effect of organic loading rate (OLR) at mesophilic temperature (35°C±2) in a long time experiment with completely stirred tank reactor (CSTR) under semi continuously feeding. The complete-mix, pilot-scale digester with working volume of 180 l operated at different organic feeding rates of 2 and 3 kg VS. (m-3.d-1). the biogas produced was measured daily by water displacement method and its composition was measured by gas chromatograph. Total solids (TS), volatile solids (VS), pH and etc. were determined according to the APHA Standard Methods. The biogas production kinetics for the description and evaluation of methanogens was carried out by fitting the experimental data of biogas production to various kinetic equations. In addition, Specific cumulative biogas production was simulated using logistic kinetic model exponential Rise to Maximum and modified Gompertz kinetic model.
Results and DiscussionThe experimental protocol was defined to examine the effect of the change in the organic loading rate on the efficiency of biogas production and to report on its steady-state performance. The biogas produced had methane composition of 58- 62% and biogas production efficiency 0.204 and 0.242 m3 biogas (kg VS input) for 2 and 3 kg VS.(m-3.d-1), respectively. The reactor showed stable performance with VS reduction of around 64 and 53% during loading rate of 2 and 3 kg VS.(m-3.d-1), respectively. Other studies showed similar results. Modified Gompertz and logistic plot equation was employed to model the biogas production at different organic feeding rates. The equation gave a good approximation of the biogas yield potential (P) and correlation coefficient (R2) over 0.99.
ConclusionsThe performance of anaerobic digestion of cow dung for biogas production using a completely stirred tank reactor was successfully examined with two different organic loading rate (OLR) under semi continuously feeding regime in mesophilic temperature range at (35°C±2). The methane content of 58- 62% and actual biogas yield of 0.204 and 0.242 m3 biogas.(kg VS input-1) were observed for 2 and 3 kg VS. (m-3.d-1), respectively. The modeling results suggested Modified Gompertz plot and Logistic growth plot both had higher correlation for simulating cumulative biogas production. Therefore, arising from the increasing environmental concern and prevailing wastes management crises, optimizing biogas production by 2 kg VS. (m-3.d-1) represents a viable and sustainable energy option.

IntroductionThe demand of pre-determined optimal coverage paths in agricultural environments have been increased due to the growing application of field robots and autonomous field machines. Also coverage path planning problem (CPP) has been extensively studied in robotics and many algorithms have been provided in many topics, but differences and limitations in agriculture lead to several different heuristic and modified adaptive methods from robotics. In this paper, a modified and enhanced version of currently used decomposition algorithm in robotics (boustrophedon cellular decomposition) has been presented as a main part of path planning systems of agricultural vehicles. Developed algorithm is based on the parallelization of the edges of the polygon representing the environment to satisfy the requirements of the problem as far as possible. This idea is based on "minimum facing to the cost making condition" in turn, it is derived from encounter concept as a basis of cost making factors.
Materials and MethodsGenerally, a line termed as a slice in boustrophedon cellular decomposition (BCD), sweeps an area in a pre-determined direction and decomposes the area only at critical points (where two segments can be extended to top and bottom of the point). Furthermore, sweep line direction does not change until the decomposition finish. To implement the BCD for parallelization method, two modifications were applied in order to provide a modified version of the boustrophedon cellular decomposition (M-BCD). In the first modification, the longest edge (base edge) is targeted, and sweep line direction is set in line with the base edge direction (sweep direction is set perpendicular to the sweep line direction). Then Sweep line moves through the environment and stops at the first (nearest) critical point. Next sweep direction will be the same as previous, If the length of those polygon's newly added edges, during the decomposition, are less than or equal to the base edge, otherwise a search is needed to choose a new base edge. This process is repeated until a complete coverage. The second modification is cutting the polygon in the location of the base edge to generate several ideal polygons beside the base edges. The algorithm was applied to a dataset (including 18 cases, ranging from simple-shaped to complex-shaped polygons) gathered from other studies and was compared with a split-merge algorithm which has been used in some other studies. The M-BCD algorithm was coded in C language using Microsoft Visual Studio 2013 software. Algorithm was run on a laptop with 2.5 GHz Intel(R) core™ i5-4200M CPU, processor with 4 GB of RAM. Also Split-merge algorithm provided by Driscoll (2011) was coded. Two algorithms were applied to the dataset. Cost of coverage plan was calculated using cost function of U-shaped turns in study Jin and Tang (2010). In this paper machine-specific parameters were working width 10 m and minimum turning radius 5 m.
Results and DiscussionBased on the results, the proposed algorithm has low computational time (below 100 ms in dataset and runs many times (on average 75 times) faster than split-merge algorithm. Algorithm resulted in a calculated savings up to 12% and on average 2% than the split-merge algorithm. Another consequence from parallelization method was effectiveness of multi-optimal direction coverage pattern than a single-optimal direction coverage; a calculated savings up to 14% and 2% on average than a single optimal direction achieved. Algorithm was evaluated on several test cases in detail. Based on the results, it is possible to loose optimal solutions especially in the case of simple shaped environments (in terms of number of convex points and internal obstacles), for example case 10 in dataset, is a case with a number of orthogonal edges. Reviewing the algorithm and Figure 4 demonstrate that sweep line moves down from the first longest edge in top of the polygon, and it doesnt stop during the process until the whole area is covered with a single coverage path direction (parallel to the longest edge). As it can be seen, no decomposition is proposed, because sweep line has faced no critical points. Based on the results in Table 2, there is 8% (equal to 88m) more cost (in term of the non-effective distance) in this case than an optimal direction and the split-merge algorithm. There are similar cases in the dataset: number 9, 12 and 13. This condition rarely occurs in complex environments, but in general it can be prevented by using an evaluation step at the end of the decomposition process. Ideally, the cost of coverage plan must be significantly less than related costs of a single optimal direction. Unlike the simple cases, algorithm returns near the optimal solution, especially in the case of complex environments. A good example of this ability of the algorithm can be seen in Figure 6. This field is case 17 in the dataset. It has many edges (almost 90 edges) and several non-convex points and an internal irregular shaped obstacle. M-BCD algorithm in a very short time (87 ms) generated several near to ideal shaped sub-regions around the field. Algorithm resulted in a calculated saving of 5% than an optimal direction with minimum non-effective distance. We can see the solution of split-merge algorithm by Oksanen and Visala (2009) in Figure 6, it can be clearly seen that coverage pattern by M-BCD is very close to the high time-consuming and optimal split-merge algorithm by Oksanen and Visala (2009). It verifies that M-BCD is efficient and optimal. There are similar test cases as hard cases in which considerable savings has been achieved (cases 6, 8 and 14).
ConclusionsIn this paper a modified decomposition algorithm as a main part of path planning systems in agricultural environments was presented. Proposed algorithm uses method of parallelization of the edges of polygon. This method is based on encounter concept and "minimum facing to cost making condition". Although the general problem had been proved to be NP-hard problem, the method has limited the search space correctly and effectively which resulted close to the optimal solutions quickly. Another advantage of the method is suitability of the solutions for any kind of machine and any polygonal flat field (and those which can be considered as flat fields).

IntroductionThe most important post-harvest mechanical damage is bruising. Bruising occurs during the stages of handling, transporting and packaging due to quasi-static and dynamic loads. Vibrations of tomato fruits during transportation by truck will decrease their quality. More than 2.5 million tons damages have been reported during tomato transportation in Iran. Therefore, it is necessary to recognize different parameters of damages during road transportation in order to detect and prevent bruising injury.
Materials and MethodsIn this study, healthy Super Queen verity of tomatoes devoid of any corrosion and mechanical damage multipliers were used. Aaverage of 7 and 5 pieces of fruit in each length and width, respectively in 13*25*37 cm boxes with a capacity of 8 kg were arranged. The boxes were divided into 2 types of truck suspension (model M2631 AIMCO, manufactured in 2010 with air suspension and Nissan trucks 2400, manufactured in 2010 with suspension spring). Boxes were established in three different heights truck, floor truck, height of middle and top of truck, in addition to two different situation boxes on the front axle (S1) and rear axle (S2). In each situation, three levels of height (H1), floor truck, the truck (H2) and the truck (H3) there. The location of each sample inside the fruit boxes bottom row (Loc1) and top (Loc2) boxes marked with marker. In this study, two types of road, highway asphalt and asphalt secondary road was used for transportation. Trucks and vans had the same distance route about 400 km. Fruits were transferred to Hamadan agricultural college. Rheology lab test was a hit with the pendulum. In this study, the amount of energy absorbed from the index (as a parameter to determine the sensitivity) and the fruits bruises were used. Hit test was done after transportation of fruits and transferring those to the laboratory in less than 2 hours. Impact energy products were considered higher than the dynamic submission, dynamic submission to the multiple ways in constant height (CHMI) were determined on the control fruits, impact energy yield limit dynamic range (0.0012) was Jules.
Results and DiscussionAnalysis of variance showed that the main factors including truck, boxes of floor height, box situation on the front and rear axles of the vehicle as well as the location of the fruit (the top and bottom of the box) has a significant effect on energy absorption. There are also some double and triple interactions energy absorbed as a factor of bruising damage in the pendulum test was significant at the 5% possibility level. Means comparison showed that the effect of the truck in height. By increasing the height from the floor of the vehicle, bruising injury increased significantly. The results showed that the fruits which transported with air suspension are healthier than those with truck suspension spring. The maximum amount of absorption energy at third height (H3) spring suspension system (T2) and rear axle (S2) with the amount respectively 491.11 and 488.59 percent increase (compared with control fruit) belong the top row fruits and bottom row fruits inside the box (in secondary asphalt), and maximum resistance bruising in the first height (H1) air suspension system (T1) and front situation (S1) with 180.42 percent increase was observed to control fruits (in highway asphalt).
The overall results show that fruit damages are low during transportation with the front axle vehicle. The results also showed that asphalt road highway and truck with air suspension system, Groups of maximum and minimum absorbed energy was more logical than truck suspension spring.

IntroductionGlobal solar radiation is the sum of direct, diffuse, and reflected solar radiation. Weather forecasts, agricultural practices, and solar equipment development are three major fields that need proper information about solar radiation. Furthermore, sun in regarded as a huge source of renewable and clean energy which can be used in numerous applications to get rid of environmental impacts of non-renewable fossil fuels. Therefore, easy and fast estimation of daily global solar radiation would play an effective role is these affairs.
Materials and MethodsThis study aimed at predicting the daily global solar radiation by means of artificial neural network (ANN) method, based on easy-to-gain weather data i.e. daily mean, minimum and maximum temperatures. Having a variety of climates with long-term valid weather data, Washington State, located at the northwestern part of USA was chosen for this purpose. It has a total number of 19 weather stations to cover all the State climates. First, a station with the largest number of valid historical weather data (Lind) was chosen to develop, validate, and test different ANN models. Three training algorithms i.e. Levenberg – Marquardt (LM), Scaled Conjugate Gradient (SCG), and Bayesian regularization (BR) were tested in one and two hidden layer networks each with up to 20 neurons to derive six best architectures. R, RMSE, MAPE, and scatter plots were considered to evaluate each network in all steps. In order to investigate the generalizability of the best six models, they were tested in other Washington State weather stations. The most accurate and general models was evaluated in an Iran sample weather station which was chosen to be Mashhad.
Results and DiscussionThe variation of MSE for the three training functions in one hidden layer models for Lind station indicated that SCG converged weights and biases in shorter time than LM, and LM did that faster than BR. It means that SCG provided the fastest performance. However, the story for accuracies was different i.e. the BR, LM, and SCG algorithms provided the most accurate performances, respectively, both among one or two hidden layers. According to the evaluation criteria, six most accurate derived models out of 1260 tested ones for Lind station was 3-14-1 and 3-11-19-1 with LM, 3-20-1 and 3-20-19-1 with BR, and 3-9-1 and 3-20-17-1 with SCG training algorithm, and 3-20-19-1 topology with BR showed the best performance out of all architectures. Results of the evaluation of the six accurate models in the remaining 18 stations of Washington State proved that regardless of the climate, in each weather station, BR with its inherent automatic regularization, provided the most accurate models (0.87 67.41 %), and then SCG (0.90>R>0.83, 3.91>RMSEMAPE > 77.28 %). Therefore, the Bayesian neural networks, which showed the best performance among all Washington State weather stations, were evaluated for Mashhad station, as an Iran sample climate. The results proved the ability of the said networks for this climate (R=0.82, RMSE=3.92¬ MJm-2, MAPE=79.92%).
ConclusionsThe results indicated that the Bayesian neural networks are capable of predicting global solar radiation with minimum inputs in different climates. This was concluded both in Washington State weather stations, which has a variety of climates, and also in Mashhad as an Iran sample weather station. These models would eliminate the need for complex climate-dependent mathematical relations or other models which are mostly dependent on many inputs. So, this algorithm would be a good means first in weather forecast practices, also in the design and development of solar assisted equipment, as well as in managerial practices in agriculture when monitoring crop solar-dependent processes like photosynthesis and evapotranspiration.

IntroductionSugarcane is an important plant in the world that cultivate for the production of sugar and energy. For this purpose, evaluation of Sugarcane (SC) and Energycane (EC) methods is necessary. Energy is vital for economic and social development and the demand for it is rising. The international community look toward alternative to fossil fuels is the aim of using liquid fuel derived from agricultural resources. According to calculations, about 47% from renewable energy sources in Brazil comes from sugarcane so as, the country is known the second largest source of renewable energy. Sugarcane in Brazil provides about 17.5% of primary energy sources. Material such as bagasse and ethanol are derived from sugarcane that provide 4.2% and 11.2 % consumed energy, respectively . In developing countries, the use of this product increase in order to achieve self-sufficiency in the production of starch and sugar and thus independence in bioethanol production. Evaluation of energy consumption in manufacturing systems, show the measurement method of yield conversion to the amount of energy. Many of products of Sugarcane have ability to produce bioenergy. Many materials obtain from sugarcane such as, cellulosic ethanol, biofuels and other chemical materials. Hence, Energycane is introduced as a new method of sugarcane harvesting. But, one of the problems of this method is high cost and high energy consumption of harvester. So that the total cost of Energycane method is 38.4 percent of production total costs, whereas, this cost, in Sugarcane method is 5.32 percent of production total costs.
In a study that was conducted by Matanker et al (2014) with title “Power requirements and field performance in harvesting EC and SC”, the power requirements of some components of sugarcane harvester and its field capacity, in Sugarcane and Energycane methods were examined. The consumed power by basecutter, elevator and chopper was measured in terms of Mega grams per hour (Mg.h-1)
Chopper energy consumption in Energycane method was 1.65 KJ more than Sugarcane method. The quantitative parameters including forward speed (km.h-1), field capacity (ha.h-1), the field performance (Mg.ha-1) and reed output (Mg.h-1) were also measured. Finally, statistical comparison was conducted between the two methods. The aim of this study is to provide Simple Additive Weighting (SAW) method using the calculated parameters by the Matanker et al. This method provides decision-making ability for a manager.
Materials and MethodsIn this study, quantitative parameters including fuel consumption (Lit.ha-1), harvester power (kW), efficiency of engine torque (%), energy of used hydraulic oil in basecutter, chopper and elevator (Mj.Mg-1), forward speed (km.h-1), field capacity (ha.h-1), the field performance (Mg.ha-1) and reed output (Mg.h-1 ) and qualitative parameters including the mean of average diameter of the stem (mm), stem height (m), number of stems on the meter (m-1), the percentage of cut stems and intact, cut stems and partially damaged and strongly damaged stems. The average height of straw and the stubble (mm), average of bulk density (kg.m-3), the average of moisture content, average of dry matter (biomass), (Mg.ha-1) were measured. Data analysis was conducted with Simple Additive Weighting (SAW) method. Tables 1 and 2 in terms of qualitative and quantitative parameters for the two methods of A and B, to form of rij matrix and based on measured criteria (C) have arranged, respectively.
ConclusionsChoosing the appropriate method for sugarcane harvesting should be according to the purpose of harvesting. Energycane method has high energy consumption that it increases the operational costs. On the other hand, the quality of the obtained biomass from it is better, but Sugarcane method has high energy efficiency. But in terms of quality, the plant is not in good condition. For this reason, it is necessary, aim of harvesting and its type, be specified before crop planting.

IntroductionBiodiesel is Fatty Acid Methyl Esters (FAME) which is used as a renewable fuel in diesel engines. Extraction of lipid from various flora sources, including Sunflower, Palm, Canola or animal oils, with a Trans-Esterification reaction between alcohol and Triglyceride (TG), leads to production of Biodiesel and Glycerin.
The production cost of biodiesel is so important that is now considered as the greatest obstacle during scale-up process.
In this research, a model-type of biodiesel production unit (using vegetable oil source), was designed by Aspen HYSYS V7.2 software, then a great deal of the attempt was employed to optimize the overall yield against the processing parameters including: mass and energy consumption load, as well as some technical discussion regarding associated apparatuses.
Process DesignThe simulation was carried out using Aspen HYSYS V7.2 employing Triolein (as TG), Oleic acid (as Free Fatty Acid (FFA)), and Oleat as biodiesel. Avoiding side-stream reactions as well as trans-esterification, the FFA content was taken to a mere 0.05% (%mass). Feed stream was considered as product of NaOH-catalyzed bi-reactor system operating at 60˚C and 1 atm with the overall conversion of 70% using two series reactors.
The ratio of TG to Alcohol is 1:3, however, owing to establish an appropriate reactor performance; this ratio was applied as 1:6 practically. The design was mainly intended to produce 480 m3d-1 biodiesel with mass concentration of 99.65%.
Methanol was used in this investigation due to low cost, accessibility and handling considerations.
NRTL was taken as the Equation of State (EOS) for the process and should be used PRSV equation in the decanter.
Thermal IntegrationEnergy consumption was taken into account as basis of optimization in this study. Table 2 demonstrates the thermal characteristics of all streams consist of source and down-streams, while outlet stream like glycerol streams were neglected to be considered. HR-1, HR-2, HD1-1, HD2-1 and HD3-1 represent cooling water leaving reactors and condensers respectively which input cooling water temperature to utility was 25˚C. Cp also indicates the thermal capacity of each line which can be calculated by multiplying mass flow rate in specific heat capacity.
In order to calculate interval temperature, as the next step, the inlet and outlet temperatures of hot flow must be diffracted from the half of minimum approach temperature of exchangers; and the inlet and outlet cold temperatures should be summed with the half of minimum of approach temperature of exchangers. Interval enthalpy can also be calculated using following equation:ΔH interval= ΔT interval [Cp Cold-Cp Hot]
Minimum approach temperature (ΔTmin) was also taken as 10°C in the following calculations. Results are shown in Table 3.
Mass IntegrationFeed stream after reaching 60˚C and 1 atm entered into first reactor. Feed streams reacted in Reac.1, and effluent after cooling to 25˚C flowed to Sep.1. Unreacted oil sent to Reac.1 and effluent of this reactor after cooling to 25 ˚C entered into Sep. 2. Products of Reac.2 including glycerin, methanol, biodiesel and oil were conveyed to Sep.2 (25˚C) for separation of ester and glycerin. The light phase (Ester) was directed to a recycle distillation column (Dist.1) with R=1.5 and 6 trays to obtain extra-pure methanol from biodiesel. Second effluents from Sep.1 and Sep.2 including large quantities of methanol and glycerin were conveyed to second distillation tower (Dist.2) with 5 tray and R=1.5 in order to purify methanol recovery and obtain glycerin purity up to 99.63%. Due to declining expenditure, methanol recycled back to the beginning of process as a feed; while glycerin was sent out to downstream as by-product.
Effluent exited from Dist.2 flowed to Sep.3 to improve purity and remove any residual catalysts (NaOH) via HCl reaction. HCl and catalyst entered with identical molar flow and reacted with 95% conversion.
The cold and hot energy required for the whole processes were calculated: 18860 kW and 17330 kW respectively.
Heat IntegrationAccording to Table 3 network required hot and cold energy were found to be zero and 17146.6 kW respectively; where the number “zero” indicates hot streams are able to provide energy needed of cold stream. Care should be taken that the exchanger approach temperature should not be less than the minimum selected approach temperature (ΔTmin).
Applying the new system in the process, cold and hot energy reduced to 17018 kW and 16670 kW respectively.
According to Figure 2, HEX-8 outlet stream temperature reached 291.8 ˚C after heat transfer. On the other hand, required temperature and heat of distillation tower’s re-boiler were 187.6 ˚C and 1858 kW respectively; therefore this could be used as energy source for the second distillation tower’s re-boilers. The output stream of the 3rd distillation tower virtual exchanger (SHD 3-out) was also important; this stream temperature was 565 ℃ that could be used to provide energy in the 1st distillation column re-boiler.
Finally cold energy and hot energy reduced by 19.6% and 38% reaching 15160 kW and 10990 kW respectively. Input and output streams of the process data and the main process flow diagram of the biodiesel process production are shown in table 4 and fig.3.
ConclusionsUsing stream recycle and mass integration methanol, unreacted oil and feed oil consumption reduced up to 60.6%, 70% and 9% respectively. Consequently, due to energy integration by exchanger network, cold and hot energy was reduced by 19.6% and 38% respectively. This integration increases the number of exchangers and pumps power due to the integration target, because the mass and heat integration targets are just reducing the mass and heat consumption. As can be seen from table 5, the number/capacity of used facilities increased in some cases as a result of application of integration method; this item can be optimized depending on economic and operating data and changing the final target to reduce overall cost, for this purpose can be used other methods such as genetic algorithms.